3D视觉结合图像检测与导纳控制的圆轴孔零件机器人装配

面向机器人柔顺装配圆轴与圆孔零件,建立基于3D、单目视觉与导纳控制的机器人自动装配系统,提出基于三维点云的轴线位姿估计算法、图像深度学习目标检测、导纳控制结合的圆轴孔零件的装配策略.针对3D视觉估计圆孔零件位姿问题,重点研究基于三维点云的轴线位姿估计算法.首先,介绍三维点云关键点选取方法;然后,以点云表面法线与轴线的几何约束为基础,提出并分析轴线粗估计的算法;最后,在轴线粗估计的基础上,提出并分析基于迭代鲁棒最小二乘的轴线位姿优化的算法.实验结果表明:轴线位姿估计的角度均方根误差为0.248°,位置均方根误差为0.463 mm,与现有流行的轴线估计方法相比,所提方法的精度更高,使装配策略很好地满足了机器人圆形轴孔零件装配的精度高、稳定可靠的要求.

Robotic assembly of cylindrical shaft and hole parts based on 3D vison, image detection and admittance control

For robotic smooth assembly of cylindrical shaft and hole parts, a system for robotic automatic assembly based on 3D vision, monocular vision and admittance control is established. The strategy for assembly of cylindrical shaft and hole parts is proposed, integrating axis pose estimation based on 3D point clouds, object detection using image deep learning, and admittance control. Aiming at the pose estimation of cylindrical hole parts based on 3D vision, an algorithm of axis pose estimation based on 3D point clouds is studied. Firstly, the method of keypoint selection on 3D point clouds is introduced. Then, based on the geometric constraints of point cloud surface normal and axis, the algorithm of coarse axis estimation is proposed and analyzed. After that, based on the coarse axis estimation, the algorithm of axis pose optimization based on iterative robust least squares is proposed and analyzed. The experimental results show that the angle RMSE of estimated axis pose is 0.248$^\circ$ and the position RMSE of that is 0.463 mm. Compared to the existing popular methods of axis estimation, the proposed method has the higher accuracy. The assembly strategy can meet the requirements of high precision, stability and reliability in robotic assembly of cylindrical shaft and hole parts.